In a design with one independent variable having three or more groups, which analysis is appropriate?

In a design with one independent variable having three or more groups, which analysis is appropriate?

• A. One-Way ANOVA
• B. Two-Way ANOVA
• C. Mann-Whitney U
• D. Wilcoxon

Answer: (A)

When you have one independent variable with three or more groups, you want to know if the mean outcome differs across those groups overall. One-way ANOVA is designed for this situation: it tests whether the group means are all equal or if at least one group mean differs from the others. It does this in one analysis, and it controls the Type I error rate better than performing multiple t-tests. If the result is significant, follow-up post hoc tests tell you which specific groups differ. The other options aren’t appropriate here because two-way ANOVA requires a second independent variable; Mann-Whitney U and Wilcoxon are nonparametric tests used for comparing two independent groups (or paired data, in the Wilcoxon case), not for three or more groups.

When you have one independent variable with three or more groups, you want to know if the mean outcome differs across those groups overall. One-way ANOVA is designed for this situation: it tests whether the group means are all equal or if at least one group mean differs from the others. It does this in one analysis, and it controls the Type I error rate better than performing multiple t-tests. If the result is significant, follow-up post hoc tests tell you which specific groups differ.

The other options aren’t appropriate here because two-way ANOVA requires a second independent variable; Mann-Whitney U and Wilcoxon are nonparametric tests used for comparing two independent groups (or paired data, in the Wilcoxon case), not for three or more groups.